DeepTabStR: Deep Learning based Table Structure Recognition

Muhammad Shoaib Ahmed Siddiqui, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, Sheraz Ahmed

In: IEEE (editor). International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition (ICDAR-2019) located at 15th September 20-25 Sydney Australia IEEE 2019.


This paper presents a novel method for the analysis of tabular structures in document images using the potential of deformable convolutional networks. In order to assess the suitability of the model to the task of table structure recognition, most of the prior methods have been tested on the smaller ICDAR-13 table structure recognition dataset comprising of just 156 tables. We curated a new image-based table structure recognition dataset, TabStructDB 1 , comprising of 1081 tables densely labeled with row and column information. Instead of collecting new images for this purpose, we leveraged the famous Page-Object Detection dataset from ICDAR-17, and added structure information for all the tabular regions present in the dataset. This new dataset will enable the development of more sophisticated table structure recognition techniques in the future. We performed an extensive evaluation on the two datasets (ICDAR-13 and TabStructDB) including cross-dataset testing in order to evaluate the efficacy of the proposed approach. We achieved state-of-the-art results with deformable models on ICDAR-13 with an average F-Measure of 92.98% (89.42% for rows and 96.55% for columns) and report baseline results on TabStructDB for guiding future research efforts with an F-Measure of 93.72% (91.26% for rows and 95.59% for columns). Despite promising results, structure recognition of tables with arbitrary layouts is still far from achievable at this point.

DeepTabStR.pdf (pdf, 1 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz